A subject vehicle has a side view mirror for each side thereof and a rear-view mirror at the front center of its cabin for a good field of view of the side and the rear needed for a change of lanes by a driver.
Although the side view mirror is used for seeing each side and the rear thereof, the subject vehicle has a blind spot where the driver cannot see a nearby vehicle or any other objects that are very close thereto.
This has been a problem because there can be an accident with the nearby vehicle in the blind spot if the driver changes lanes without seeing the nearby vehicle.
To prevent such a problem, the driver sometimes put a convex mirror onto a corner of the side view mirror, which enables the driver to see the blind spot.
However, even when the convex mirror is added onto the side view mirror, the driver must see the blind spot with his/her own eyes to change lanes, which puts further strain to the driver, and there may exist part of the blind spot that still cannot be seen through the convex mirror even if the driver alters his/her head position.
To prevent this, a blind spot monitoring system is suggested recently that aims to prevent accidents from happening when the driver changes lanes, by providing the driver with information on a detection of a monitored vehicle located in the blind spot or approaching the blind spot through a sensor placed at the rear of a monitoring vehicle. Especially, blind spot monitoring systems using a vision sensor generally adopt various algorithms capable of detecting objects based on visual information.
However, those algorithms may show limited detection rates constrained by external environment, shape of the objects, and a configuration of a system. Because an accurate detection requires a number of visual processing, a computational load is very heavy. Therefore, real-time detection may be difficult in an embedded system due to limited processing resources.
As one example of detecting the monitored vehicle using the vision sensor, there is an optical flow method which expresses movement of visual pixels by motion vectors. However, an algorithm for recognition of the monitored vehicle using the optical flow method has much dependence on a change of a state of a background and a visual noise, and requires an enormous computational load, therefore, real-time detection of the monitored vehicle is not easy.
Further, a conventional blind spot monitoring system using the vision sensor has a problem of false alarm because the system gives a mindless warning of the monitored vehicle located in the blind spot, without taking into consideration of driving environment of the monitoring vehicle and the monitored vehicle.
For example, the monitored vehicle on the same lane following the monitoring vehicle on a curved road is falsely detected as located in the blind spot, and the monitored vehicle traveling in the opposite direction on the opposite side of the lane is also falsely detected as located in the blind spot.